Senior ML Ops Engineer (AI)

TL;DR

Senior Ml Ops Engineer (AI): Automating and orchestrating machine learning workflows across major cloud and AI platforms, turning experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Focus on designing and implementing engineering components of GAR+RAG systems, managing prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted.

Location: Home based-Connecticut, Home based-Georgia, Home based-New Jersey, Home based-Virginia, Must be based in the USA

Salary: $95,300 - $171,954 (depending on location)

Company

hirify.global is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services.

What you will do

• Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI).
• Maintain and version model registries and artifact stores to ensure reproducibility and governance.
• Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment.
• Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML.
• Scale end-end custom Sagemaker pipelines.
• Design and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted.

Requirements

• Current experience in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
• Strong Python, Java, and/or Scala experience will be considered a plus.
• Hands-on‑ experience with major cloud vendor solutions (AWS, Azure and/or Google)
• Experience with Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr / Neo4j).
• Experience in evaluating LLM models.
• A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics.

Nice to have

• Background in health technology and/or medical content workflows is preferred.
• Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark.
• Experience with large-scale data processing systems, e.g., Spark.
• Experience with statistical analysis, machine learning theory and natural language processing.

Culture & Benefits

• Company offers country specific benefits.
• Committed to providing a fair and accessible hiring process.
• Equal opportunity employer.